Summary: Common to most correlation analysis techniques for neuronal spiking activity are assumptions of stationarity with respect to various parameters. However, experimental data may fail to be compatible with these assumptions. This failure can lead to falsely assigned significant outcomes. Here we study the effect of nonstationarity of spike rate across trials in a model-based approach. Using a two-rate-state model, where rates are drawn independently for trials and neurons, we show in detail that nonstationarity across trials induces apparent covariation of spike rates identified as the generator of false positives. This finding has specific implications for the “shuffle predictor.” Within the framework developed for our model, covariation of spike rates and the mechanism by which the shuffle predictor leads to wrong interpretation of the data can be discussed. Corrections for the influence of nonstationarity across trials by improvements of the predictor are presented.